{"id":"04ed243e-7089-45ff-a476-e28ff1abc009","arxiv_id":"2605.18843","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"TEMPO uses a leakage-first then performance reward with GRPO training to reduce post-cutoff knowledge leakage in LLM backtesting from 2-13% to 0.6-3.7% while preserving or improving task performance on three prediction tasks.","lead":"The paper introduces TEMPO, a reinforcement learning approach that trains LLMs to reason only from pre-cutoff information during backtesting by using a two-stage reward that first eliminates post-cutoff leakage before optimizing task accuracy. If effective, this could make historical evaluations of language models more trustworthy by removing a common source of inflated performance.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Proof of monotonic leakage decrease assumes strict mode separation that GRPO may not enforce without side effects","rationale":"The reader's weakest_assumption matches the load-bearing point exactly. The abstract-only limitation noted by the reader makes the proof the natural place to probe; confirming or refuting the derivation would directly settle whether the reported leakage reductions and performance gains are robust.","tokens_in":1760,"tokens_out":307,"duration_ms":21588,"concrete_test":"Extract the exact two-mode reward definition and the monotonicity proof from the methods section; re-derive the decrease step without assuming sequential gating. If the derivation requires an unstated assumption (e.g., leakage reward dominates with infinite weight or modes are never co-active), the convergence claim does not hold.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim rests on proving that training monotonically decreases leakage and converges to the leak-free optimum via the two-mode reward (leakage mode as hard prerequisite before performance mode). In GRPO-based optimization, rewards are typically scalarized or scheduled rather than strictly gated; if the performance term can exert gradient influence while leakage remains positive, or if the model discovers indirect leakage strategies that evade the leakage-mode penalty, monotonicity fails and new failure modes (e.g., performance collapse or spurious compliance) can appear. This is the least secure link because the abstract states the proof but does not detail the exact reward combination or mode-switching rule that would make the prerequisite truly hard.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes TEMPO, a GRPO-based training method for LLMs that uses a two-mode reward structure (leakage mode as hard prerequisite before performance mode) to enforce temporal compliance and prevent post-cutoff knowledge leakage in backtesting tasks. It claims to prove that training monotonically decreases leakage and converges to the leak-free optimum, with empirical results across three prediction tasks and two models showing leakage reduced from 2-13% to 0.6-3.7% and task performance improved by 6-13% where strong pre-cutoff signals exist.","tokens_in":1915,"tokens_out":487,"duration_ms":25969,"significance":"If the central claims hold, this addresses a practical and theoretically interesting problem in trustworthy LLM evaluation by learning instance-specific temporal discipline rather than relying on unlearning or prompts. The mode-separated reward and claimed monotonic convergence proof could generalize to other compliance settings. The empirical reductions in leakage with maintained or improved performance on multiple tasks provide a concrete demonstration of the approach.","major_comments":[{"comment":"Abstract: The stated proof of monotonic leakage decrease and convergence to the leak-free optimum via the two-mode reward assumes strict mode separation with leakage mode as a hard prerequisite. However, the abstract provides no derivation or exact reward combination/mode-switching rule, leaving open whether GRPO scalarization allows performance gradients to influence the policy while leakage remains positive.","section":"Abstract"},{"comment":"Abstract and §4 (implied experimental section): Performance gains of 6-13% are reported only in conditions where leakage compliance is first achieved. This raises the possibility that improvements are conditional on successful suppression rather than independently validated; external benchmarks independent of the leakage metric are not shown to confirm the gains are not artifacts of the reward gating.","section":"Abstract"}],"minor_comments":[{"comment":"The description of how leakage is quantified (e.g., exact detection of post-cutoff claims) and the precise implementation of the two-mode reward function should be expanded for reproducibility.","section":"Experiments"},{"comment":"Notation for the reward modes and GRPO update rule could be clarified with an explicit equation showing the gating mechanism.","section":"Method"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive and insightful comments, which help us clarify key aspects of the TEMPO method and its presentation. We address each major comment below and indicate planned revisions to the manuscript.","responses":[{"response":"We agree that the abstract, constrained by length, omits the explicit reward formulation and mode-switching rule that underpin the proof. Section 3 of the manuscript defines the two-mode reward with leakage mode as a strict prerequisite (performance reward is gated at zero until leakage reaches zero for the instance), after which the policy transitions to performance mode; GRPO updates are applied sequentially to enforce this separation and prevent performance gradients from acting while leakage remains positive. The monotonic decrease and convergence follow from this gated structure. In revision we will add one sentence to the abstract summarizing the reward combination and hard-prerequisite rule to make the proof assumptions explicit.","revision_made":"yes","referee_comment":"[Abstract] Abstract: The stated proof of monotonic leakage decrease and convergence to the leak-free optimum via the two-mode reward assumes strict mode separation with leakage mode as a hard prerequisite. However, the abstract provides no derivation or exact reward combination/mode-switching rule, leaving open whether GRPO scalarization allows performance gradients to influence the policy while leakage remains positive."},{"response":"The sequential structure is intentional: leakage compliance is enforced first precisely so that subsequent performance gains reflect reasoning from pre-cutoff information alone. Task performance is measured by standard prediction accuracy on the three backtesting tasks, a metric computed independently of the leakage detector. Gains appear only where strong pre-cutoff signals exist because that is when valid temporal discipline yields measurable benefit; where signals are weak, performance is maintained rather than inflated. We will revise the experimental section to state explicitly that accuracy is evaluated separately from leakage scoring and to include a short analysis correlating gains with the presence of pre-cutoff evidence. No new external benchmarks are required for the core claim, but we can add a clarifying paragraph if the referee indicates a preferred reference task.","revision_made":"partial","referee_comment":"[Abstract] Abstract and §4 (implied experimental section): Performance gains of 6-13% are reported only in conditions where leakage compliance is first achieved. This raises the possibility that improvements are conditional on successful suppression rather than independently validated; external benchmarks independent of the leakage metric are not shown to confirm the gains are not artifacts of the reward gating."}],"tokens_in":1435,"tokens_out":520,"duration_ms":36725,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing here is that TEMPO trains LLMs for historical backtesting by splitting the reward into a leakage mode that must hit zero first, then a performance mode, all inside a GRPO loop, with a proof that leakage falls monotonically to the leak-free point and accuracy rises once compliance is locked in. Experiments on three tasks and two models show leakage dropping from 2-13% to 0.6-3.7% and task scores up 6-13% when pre-cutoff signals are strong. That is a direct, measurable fix for a problem that prompt constraints and unlearning both miss because the cutoff is query-specific. The framing is clear on why those older approaches fail when facts are causally tied to the prediction. The empirical numbers are reported consistently across conditions and look like real progress for anyone running time-sensitive LLM evaluations. The soft spot is the proof itself. It rests on the leakage mode functioning as a strict gate so the performance term cannot pull gradients while leakage is still positive. GRPO usually scalarizes or schedules rewards rather than enforcing hard prerequisites, so it is not obvious how indirect leakage paths or temporary compliance are ruled out without side effects such as performance collapse or spurious strategies. The abstract states the convergence result but does not spell out the exact reward combination or switching rule, which leaves the monotonicity claim harder to verify from the given details. The gains are shown only after leakage is suppressed, which matches the method but does not test whether the same performance lift appears under weaker enforcement. This is aimed at people who build or audit forecasting and historical benchmarks for LLMs. Readers working on RL for temporal reasoning or evaluation hygiene would find the concrete setup useful to try or extend. I would send it to peer review. The problem is practical, the experimental deltas are worth checking, and the core idea is grounded enough that referees can pressure-test the mode separation and proof details in revision.","headline":"TEMPO uses a two-mode GRPO setup to gate leakage suppression before performance gains and claims a monotonic convergence proof, but the hard prerequisite may not hold cleanly in the optimizer.","tokens_in":2405,"tokens_out":464,"would_cite":false,"duration_ms":48129,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[],"headline":"TEMPO two-mode GRPO reward with hard leakage prerequisite is orthogonal to RS J-cost forcing","alignment":"orthogonal","rationale":"Paper centers on instance-specific temporal discipline via group-level mode gate (leakage mode exp(-0.5 n_leak) as hard prerequisite before performance mode) and GRPO convergence proofs (Prop. 1 gradient alignment, Thm. 1 monotonic linear convergence under PL). RS derives spacetime/constants from single distinction via J(x)=½(x+x⁻¹)-1 uniqueness, φ-ladder, 8-tick periodicity (reality_from_one_distinction, AbsoluteFloorClosure, Cost.FunctionalEquation). No shared machinery: no ratio-symmetric cost, no parameter-free constant derivation, no 8-period clock. Domain mismatch (applied LLM backtesting vs foundational logic-to-physics forcing) yields no alignment.","tokens_in":59653,"confidence":"high","tokens_out":192,"duration_ms":14349,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"TEMPO trains LLMs to suppress post-cutoff leakage as a strict prerequisite before optimizing prediction accuracy in historical backtests.","keywords":["LLM backtesting","temporal compliance","leakage reduction","mode-separated reward","policy optimization","historical prediction","temporal discipline"],"falsifier":"A replication experiment on one of the three prediction tasks in which leakage after TEMPO training remains above 3.7% or task performance fails to improve or maintain in conditions that contain strong pre-cutoff signals.","tokens_in":2655,"feed_emoji":"","tokens_out":743,"duration_ms":40942,"temperature":0.7,"pith_summary":"The paper shows that LLMs routinely incorporate future knowledge when reasoning about past events, which inflates their measured performance and makes backtesting unreliable. Rather than trying to erase knowledge or rely on prompts, the authors introduce a training procedure that forces the model to select only pre-cutoff evidence for each specific instance. This is achieved by first driving leakage to zero through a dedicated reward mode, then allowing performance optimization only after compliance is met. If the method works as described, backtesting results would reflect genuine temporal reasoning instead of training-data contamination. A reader would care because trustworthy evaluation is required before LLMs can be used for forecasting or any decision that depends on what was known at a given time.","feed_headline":"Two-mode training cuts LLM leakage in backtests to 0.6-3.7%","feed_subtitle":"Leakage suppression is enforced as a prerequisite before any performance gains are rewarded, producing trustworthy historical evaluations.","key_machinery":"The two-mode reward structure that treats leakage suppression as a non-negotiable first stage before any performance reward is applied.","core_discovery":"TEMPO introduces a two-mode reward structure in which a leakage mode drives all post-cutoff claims to zero as a hard prerequisite, after which a performance mode optimizes task accuracy, paired with a GRPO-based training pipeline that lets the model discover valid reasoning paths. The authors prove that this process monotonically decreases leakage, converges to the leak-free optimum, and improves task performance once temporal compliance is reached. Experiments on three prediction tasks and two models confirm leakage falls from 2-13% to 0.6-3.7% while performance rises 6-13% where strong pre-cutoff signals exist and holds steady where the task is difficult from valid information alone.","pith_inferences":["The same prerequisite-mode structure could be applied to other instance-specific constraints, such as jurisdiction or regulatory regime, where certain facts are valid only under particular conditions.","Once leakage is reliably controlled, backtesting could be extended to longer historical horizons or to multi-step forecasting sequences without the current risk of contamination.","Models trained this way might generalize to real-time deployment where the cutoff date is the current moment, enabling safer use in live forecasting systems."],"forward_implications":["Leakage rates drop from the observed 2-13% range to 0.6-3.7% across all tested conditions.","Task performance improves by 6-13% on predictions that have clear pre-cutoff signals.","Performance is preserved rather than degraded on tasks that remain difficult even with only pre-cutoff information.","Training exhibits monotonic leakage reduction and converges to the leak-free optimum."],"fun_headline_variants":["Two-mode rewards enforce zero leakage prior to backtest performance gains","TEMPO training monotonically decreases LLM leakage in historical predictions","Mode-separated optimization yields 0.6-3.7% leakage across LLM backtests","GRPO pipeline allows discovery of temporally valid reasoning strategies"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The leakage mode can be implemented as a hard prerequisite that does not distort or interfere with subsequent performance optimization.","fun_headline_variants_meta":{"raw":{"variants":["Two-mode rewards enforce zero leakage prior to backtest performance gains","TEMPO training monotonically decreases LLM leakage in historical predictions","Mode-separated optimization yields 0.6-3.7% leakage across LLM backtests","GRPO pipeline allows discovery of temporally valid reasoning strategies"]},"model":"grok-4.3","cost_usd":0.010445,"raw_usage":{"total_tokens":4586,"prompt_tokens":762,"num_sources_used":0,"completion_tokens":71,"cost_in_usd_ticks":104453000,"prompt_tokens_details":{"text_tokens":762,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3753,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":762,"tokens_out":71,"duration_ms":47001,"temperature":1.0,"reasoning_tokens":3753,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-20T21:19:15.042288+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A replication experiment on one of the three prediction tasks in which leakage after TEMPO training remains above 3.7% or task performance fails to improve or maintain in conditions that contain strong pre-cutoff signals.","supporting_citations":[],"review_version":1}